Information processing apparatus, information processing method, and non-transitory computer-readable storage medium

ABSTRACT

An information processing apparatus (100) includes a collation unit (102) that collates first feature information extracted from a person included in a first image (10) with first feature information indicating a feature of a retrieval target person, an extraction unit (104) that extracts second feature information from the person included in the first image in a case where a collation result in the collation unit (102) indicates a match, and a registration unit (106) that stores, in a second feature information storage unit (110), the second feature information extracted from the person included in the first image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patentapplication Ser. No. 17/413,789 filed on Jun. 14, 2021, which is aNational Stage Entry of international application PCT/JP2018/048088,filed on Dec. 27, 2018, the disclosures of all of which are incorporatedin their entirety by reference herein.

TECHNICAL FIELD

This disclosure relates to an information processing system, aninformation processing apparatus, an information processing method, anda program, capable of performing an image recognition process.

BACKGROUND ART

In recent years, a technique of detecting a person matching a desiredcondition by using an image has been developed. For example, PatentDocument 1 clos a system which judges whether or not a predeterminedperson such as a missing person or a wanted criminal is included inmoving images captured in a street or the like. The system generatesfeature data of a face image included in each frame of captured images,compares pieces of the generated feature data, and thus sorts thefeature data for each person such that feature data of an identicalperson is included in a single group. The system determinesrepresentative feature data for each person on the basis of the sortedfeature data, and transmits at least one of the determined feature dataand a face image corresponding to the determined feature data to a facerecognition apparatus as representative face data. The face recognitionapparatus collates the transmitted data with face data of apredetermined person registered in a face data dictionary.

Patent Document 2 discloses an image processing apparatus whichidentifies an identical person from images captured by differentcameras, and thus automatically tracks the person. The different camerascapture images of the person from different directions.

Patent Document 3 discloses a technique of detecting a walking state ofa person from temporally-distant frames or person image sequencesobtained by different cameras, and judging whether or not personsincluded in different image sequences are the same person on the basisof the walking state.

RELATED DOCUMENTS Patent Documents

[Patent Document 1] Japanese Unexamined Patent Application PublicationNo. 2017-182210

[Patent Document 2] Japanese Unexamined Patent Application PublicationNo. 2015-2547

[Patent Document 3] International Publication No. WO2006/013765

SUMMARY OF THE INVENTION Technical Problem

The present inventor has examined a new technique for identifying aperson who cannot be identified in an original image by using imageprocessing on other images. In other words, an object of this disclosureis to provide a technique for identifying a person who cannot beidentified in an original image by using image processing on otherimages.

Solution to Problem

In each aspect of this disclosure, the following configuration isemployed to solve the above-described problem.

A first aspect relates to an information processing apparatus.

An information processing apparatus related to the first aspect includesa collation unit that collates first feature information extracted froma person included in a first image with first feature informationindicating a feature of a retrieval target person; an extraction unitthat extracts second feature information from the person included in thefirst image in a case where a collation result in the collation unitindicates a match; and a registration unit that stores, in a storageunit, the second feature information extracted from the person includedin the first image.

A second aspect relates to an information processing method executed byat least one computer.

A first information processing method related to the second aspect,executed by an information processing apparatus, includes collatingfirst feature information extracted from a person included in a firstimage with first feature information indicating a feature of a retrievaltarget person, extracting second feature information from the personincluded in the first image in a case where a collation result indicatesa match, and storing, in a storage unit, the second feature informationextracted from the person included in the first image.

It should be noted that other aspects of this disclosure may relate to aprogram causing at least one computer to execute the method of thesecond aspect, and may relate to a computer readable storage mediumstoring such a program. The storage medium includes a non-transitorymedium.

The computer program includes computer program codes causing a computerto execute the control method of the information processing apparatus onthe information processing apparatus when the program is executed by thecomputer.

It should be noted that any combination of the above-describedconstituent elements, and expressional conversion of this disclosureamong a method, an apparatus, a system, a storage medium, a computerprogram, and the like is also effective as an aspect of this disclosure.

Various constituent elements of this disclosure are not necessarilyrequired to be individually independent elements. For example, aplurality of constituent elements may be configured as a single member,a single constituent element may be configured with a plurality ofmembers, any constituent element may be a part of another constituentelement, and a part of any constituent element may overlap a part ofanother constituent element.

A plurality of procedures are sequentially described in the method andthe computer program of this disclosure, but the order of descriptiondoes not limit an order of executing the plurality of procedures. Thus,in a case where the method and the computer program of this disclosureare executed, the order of the plurality of procedures may be changedwithin the scope without contradiction to contents thereof.

The plurality of procedures of the method and the computer program ofthis disclosure are not limited to being respectively executed atdifferent timings. Thus, another procedure may occur during execution ofany procedure, and an execution timing of any procedure may partially orentirely overlap an execution timing of another procedure.

Advantageous Effects of Invention

According to each aspect above, it is possible to provide a techniquefor identifying a person who cannot be identified in an original imageby using image processing on other images.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-described object, and other objects, features, and advantageswill become apparent throughout preferable example embodiments describedbelow and the accompanying drawings.

FIG. 1 is a conceptual diagram illustrating a configuration of an imageprocessing system according to an example embodiment of this disclosure.

FIG. 2A is a diagram illustrating an example of a data structure ofvideo data, and FIG. 2B is a diagram illustrating an example of a datastructure of image data.

FIG. 3 is a functional block diagram illustrating a logicalconfiguration of an information processing apparatus according to afirst example embodiment.

FIG. 4A is a diagram illustrating for explaining a second image, andFIGS. 4B and 4C are diagrams each illustrating for explaining a firstimage.

FIG. 5 is a flowchart illustrating an example of procedures of a processof the information processing apparatus generating first featureinformation.

FIG. 6A is a diagram illustrating an example of a data structure of afirst feature information storage unit, and FIGS. 6B and 6C are diagramseach illustrating an example of a data structure of a second featureinformation storage unit.

FIG. 7 is a flowchart illustrating an example of an operation of theinformation processing apparatus.

FIG. 8 is a flowchart illustrating details of a retrieval process instep S110 in FIG. 7 .

FIG. 9 is a functional block diagram illustrating a logicalconfiguration of an information processing apparatus according to asecond example embodiment.

FIG. 10 is a diagram illustrating an example of a data structure of arecognition information database.

FIG. 11 is a flowchart illustrating an example of procedures of arecognition process in the information processing apparatus.

FIG. 12 is a flowchart illustrating another example of an operation ofthe information processing apparatus.

FIG. 13 is a diagram illustrating an example of a data structure of asecond feature information storage unit.

FIG. 14 is a functional block diagram illustrating a logicalconfiguration of an information processing apparatus according to athird example embodiment.

FIG. 15 is a diagram illustrating an example of a result list screendisplayed by the information processing apparatus.

FIG. 16 is a diagram illustrating an example of a data structure of arecognition result storage unit.

FIG. 17 is a flowchart illustrating an example of an operation of theinformation processing apparatus.

FIG. 18 is a flowchart illustrating an example of detailed procedures ofa display process in step S130 in FIG. 17 .

FIG. 19 is a diagram illustrating details of a display example in acandidate display section in FIG. 15 .

FIG. 20 is a diagram illustrating details of a display example in acandidate display section in FIG. 15 .

FIG. 21 is a diagram illustrating an example of a detailed informationwindow.

FIG. 22 is a diagram illustrating an example of procedures of a displayprocess in the information processing apparatus.

FIG. 23 is a diagram illustrating an example of procedures of a displayprocess in the information processing apparatus.

FIG. 24 is a diagram illustrating an example of procedures of a displayprocess in the information processing apparatus.

FIGS. 25A to 25F are diagrams for explaining changes of a displayscreen.

FIG. 26 is a diagram illustrating an example of a computer implementingthe information processing apparatus of each example embodiment.

DESCRIPTION OF EMBODIMENTS

Hereinafter, example embodiments of this disclosure will be describedwith reference to the drawings. The same constituent elements are giventhe same reference numerals throughout all the drawings, and descriptionthereof will not be repeated as appropriate.

In each drawing of the present specification, a configuration of aportion having no relation to the essence of this disclosure is omittedand is not illustrated.

First Example Embodiment

FIG. 1 is a conceptual diagram illustrating a configuration of an imageprocessing system according to an example embodiment of this disclosure.

An image processing system 1 includes an information processingapparatus 100. The information processing apparatus 100 retrieves acertain person from images captured by a plurality of cameras 5 a, 5 b,5 c, . . . (referred to as a “camera 5” or “cameras 5” in a case wherethe cameras are not particularly required to be differentiated from eachother).

For example, there is a case where only a retrieval target person's backis captured in, for example, an original image (second image 22), andthus face recognition cannot be performed. In this case, the informationprocessing apparatus 100 finds a matching candidate, who resembles theretrieval target person, from other images (first images 10) captured bythe cameras 5 by using information (hereinafter, referred to as firstfeature information) such as a physique, and the person is identifiedthrough face recognition by using an image in which the face thereof isshown among the images.

It should be noted that, in the example embodiment, the “acquisition”includes at least one of an apparatus fetching (active acquisition) dataor information stored in another apparatus or a storage medium and theapparatus inputting (passive acquisition) therein data or informationwhich is output from another apparatus. As an example of the activeacquisition, there are a case where an apparatus sends a request or aninquiry to another apparatus, and receives a response thereto, and acase where the apparatus accesses another apparatus or a storage medium,and reads data or information. As an example of the passive acquisition,there is a case where a host apparatus receives delivered information(alternatively, transmitted information or information sent through pushnotification). The “acquisition” may include selectively acquiring dataor information from received data or information, or selectivelyreceiving delivered data or information.

The camera 5 is a camera including a lens and an imaging element such asa charge coupled device (CCD) image sensor. The camera 5 is, forexample, a network camera such as an Internet Protocol (IP) camera. Thenetwork camera has, for example, a wireless local area network (LAN)communication function, and is connected to the information processingapparatus 100 through a communication network, that is, a relayapparatus (not illustrated) such as a router. The cameras 5 may beso-called surveillance cameras provided in a street or inside andoutside a building. The camera 5 may include a mechanism which tracksmovement of a specific person in accordance with the movement, andperforms control of a camera main body or a direction of a lens, zoomcontrol, or focusing.

The camera 5 and the information processing apparatus 100 may not beconnected to each other, may be directly connected to each other, andmay be indirectly connected to each other through a communicationnetwork 3 or the like. In a case where the camera 5 and the informationprocessing apparatus 100 are not connected to each other, for example,image data is read and acquired from a medium 7 a and a medium 7 b(hereinafter, referred to as a “medium 7” or “media 7” in a case wherethe media are not particularly required to be differentiated from eachother) which respectively store images captured by the camera 5 a andthe camera 5 b.

The camera 5 c in an example of being connected to the informationprocessing apparatus 100 through the communication network 3 or the likehas, for example, a wireless local area network (LAN) communicationfunction, and is connected to the information processing apparatus 100through the communication network 3, that is, a relay apparatus (notillustrated) such as a router. The camera 5 c is preferably a networkcamera such as an Internet Protocol (IP) camera.

Regarding a timing at which an image is transmitted from the camera 5 tothe information processing apparatus 100, an image may be delivered inreal time, for example, through streaming delivery, and imagescorresponding to a predetermined period may be transmitted at apredetermined interval. The transmission timing may be selected asappropriate on the basis of a memory capacity, a communication capacity,or image processing performance of the camera 5 or the informationprocessing apparatus 100, or a communication situation or the likebetween the camera 5 and the information processing apparatus 100, andmay be changed depending on a situation change.

For example, image data captured by the camera 5 may be directlytransmitted to the information processing apparatus 100, and theinformation processing apparatus 100 may sequentially receive the imagedata. A storage apparatus (not illustrated) which can be accessed byboth of the camera 5 and the information processing apparatus 100 may beprovided. In this case, image data captured by the camera 5 is stored inthe storage apparatus. The information processing apparatus 100 readsthe image data from the storage apparatus.

Here, the image data may be at least one of a still image and a movingimage. A data format, a file format, a file size, an image size, aresolution of an image, a frame rate of moving images, and the like arenot particularly limited, and data or files of various formats may beemployed according to specifications, standards, performance, and thelike of the camera 5 and the information processing apparatus 100, orimage analysis processing performance or accuracy thereof. At least oneframe of the image data may be at least one of a first image 10 and asecond image 22 which will be described later.

FIG. 2A is a diagram illustrating an example of a data structure ofvideo data 40. The video data 40 is captured by a certain camera 5, andis received from the camera 5 or is stored on the medium 7. The videodata 40 includes pieces of information indicating a video ID foridentifying the video data 40, a medium ID for identifying the medium 7storing the video data 40 or a camera ID for identifying the camera 5capturing the video data 40, and imaging conditions (for example, animaging location and imaging time) of the camera 5, and the entity ofthe video data.

FIG. 2B is a diagram illustrating an example of a data structure of thefirst image 10. The first image 10 is a part of the video data 40captured by a certain camera 5, and may be any frame of the video data40. The first image 10 includes pieces of information indicating animage ID for identifying the image and a video ID for identifying thevideo data 40 which is the source of the image, and the entity of imagedata. In a case where the entity of image data is a frame, the firstimage 10 further includes information indicating a frame number foridentifying the frame. The image ID may be a file name. It should benoted that the first image 10 is an image from which a retrieval targetperson is retrieved unlike the second image 22. The first image 10 maybe captured by a camera 5 which is different from a camera capturing thesecond image 22.

FIG. 3 is a functional block diagram illustrating a logicalconfiguration of the information processing apparatus 100 of the presentexample embodiment. The information processing apparatus 100 includes acollation unit 102, an extraction unit 104, and a registration unit 106.In the example illustrated in FIG. 3 , the information processingapparatus 100 further includes a second feature information storage unit110. The second feature information storage unit 110 may be integratedwith a main body of the information processing apparatus 100, and may beseparated therefrom. The second feature information storage unit 110 mayhave a database structure, and may have other data structures.

The collation unit 102 performs a collation process of collating firstfeature information indicating a feature of a retrieval target personwith feature information of a person region extracted from a personincluded in the first image 10. Here, first feature informationextracted from the second image 22 is feature information indicating afeature of a person region of a retrieval target person, and will alsobe hereinafter first feature information 14 a. Feature information of aperson region extracted from a person included in the retrieval targetfirst image 10 will also be hereinafter referred to as first featureinformation 14 b. In a case where a collation result in the collationunit 102 indicates a match, the extraction unit 104 extracts secondfeature information from a person included in the first image 10. Theregistration unit 106 stores the second feature information extractedfrom the person included in the first image 10 in the second featureinformation storage unit 110. In other words, the registration unit 106registers the second feature information extracted from the personincluded in the first image 10 in the second feature information storageunit 110.

Specifically, the collation unit 102 acquires the first image 10 byreading the first image 10 from the medium 7 or by receiving the firstimage 10 from the camera 5. First, the collation unit 102 specifies aperson region from the acquired first image 10, and performs a collationprocess with the first feature information 14 a on the specified personregion. Here, there may be a plurality of pieces of first featureinformation 14 a.

FIG. 4A illustrates an example of the second image 22. The second image22 is an image in which a person is captured, but the person cannot beidentified through face recognition or the like. The second image 22 is,for example, an image in which a person faces backward, and thus theface thereof is not captured, an image in which a face is not capturedto the extent to which matching accuracy of a reference value or morecan be obtained in a face recognition, or a low quality image on whichface recognition cannot be performed. It should be noted that the imagein which a person cannot be identified is not limited to a case whereface recognition cannot be performed. The image may be an image in whicha person cannot be identified in other recognition methods. The otherrecognition methods may be, for example, biological recognition, and isrecognition using at least one of an iris, a pinna, a vein, and afingerprint. With respect to a person captured in the second image 22,instead of identifying the person through a face recognition process orother recognition processes, the first feature information 14 a may beextracted from a person region of the person. In other words, a facerecognition process or other recognition processes are not necessarilyrequired to be performed on the second image 22. In other words, thesecond image 22 may be an image on which identification of a personcaptured therein is not performed.

In a collation process, the collation unit 102 judges a person region ofwhich the matching degree with the first feature information 14 a isequal to or more than a reference, to be a region corresponding to thefirst feature information 14 a. The extraction unit 104 specifies aperson region of which the matching degree with the first featureinformation 14 a is equal to or more than a reference in the first image10, and further specifies a facial region of the person region.

The matching degree is a value indicating the degree of matching, and,in the present example embodiment, the matching degree is a numericalvalue of 0 to 1, is 1 in a case of complete matching, and is 0 in a caseof complete mismatching.

The extraction unit 104 extracts feature information of the specifiedfacial region so as to generate second feature information, and theregistration unit 106 stores the second feature information in thesecond feature information storage unit 110. As illustrated in FIGS. 4Band 4C, a person region 16 and a facial region 20 are specified in thefirst image 10. A person including the regions is a candidate of theretrieval target person.

The extraction unit 104 may extract pieces of the first featureinformation 14 a of a plurality of retrieval target persons from personregions of persons included in a plurality of different second images22, and the registration unit 106 may store a plurality of the pieces offirst feature information 14 a in a first feature information storageunit 112 in association with each other as information regarding anidentical person. The collation unit 102 may perform a collation processby using the plurality of pieces of first feature information 14 a.

The information processing apparatus 100 is not necessarily required toinclude the first feature information storage unit 112. The collationunit 102 may acquire the first feature information 14 a by reading thefirst feature information 14 a from the first feature informationstorage unit 112, or by receiving the first feature information 14 ainputted from an external apparatus. The first feature informationstorage unit 112 may also be used as the second feature informationstorage unit 110. In this case, the information processing apparatus 100uses the generated second feature information as the first featureinformation.

In the present example embodiment, the person region includes regionscorresponding to a first portion and a second portion. The secondportion is at least one region of, for example, a face, an iris, apinna, a vein, and a fingerprint. Alternatively, the second portion maybe a gait of a person instead of a region. The first portion may or notinclude the second portion. In the present example embodiment, the firstportion is a region of the whole body of the person, and the secondportion is a facial region. The first feature information (the firstfeature information 14 a and the first feature information 14 b) of thefirst portion is feature information indicating an appearance featuresuch as a size or a costume of a person. In the present specification,in some cases, the first feature information will be referred to as“person region feature information”, and the second feature informationwill be referred to as “face information”.

The first feature information (the first feature information 14 a andthe first feature information 14 b) includes information indicatingfeatures such as a height, a shoulder width, a body part ratio, agarment (a shape, a color, a material, or the like), a hair style (alsoincluding a hair color), an ornament (a cap, spectacles, an accessory,or the like), and a belonging (a bag, an umbrella, or a stick). Theperson region feature information may include information such aslikelihood of the feature information.

In the present example embodiment, the first feature information 14 a ofa retrieval target person is generated by using the second image 22 inwhich the retrieval target person is captured. The second image 22 maybe an image captured by the camera 5, may be an image captured by otherimaging means, and may be an image read by a scanner.

The information processing apparatus 100 further includes a firstfeature information generation unit (not illustrated). The first featureinformation generation unit generates the first feature information 14 aby using the second image 22 in which a retrieval target person iscaptured, and stores the first feature information 14 a in the firstfeature information storage unit 112.

FIG. 5 is a flowchart illustrating an example of procedures of a processof the information processing apparatus 100 generating the first featureinformation 14 a. The first feature information generation unit acquiresan image (second image 22) of a retrieval target person (step S10),generates the first feature information (person region featureinformation) by using the acquired image, and stores the second featureinformation in the first feature information storage unit 112 (stepS20).

FIG. 6A is a diagram illustrating an example of a data structure of thefirst feature information storage unit 112. A retrieval target ID foridentifying a retrieval target person, the first feature information 14a of the person, and image data of an original image from which thefirst feature information 14 a is acquired are stored in the firstfeature information storage unit 112 in association with each other. Thefirst feature information storage unit 112 may store the entity of imagedata, and may store a path indicating a location where image data ispreserved and a file name.

FIG. 6B is a diagram illustrating an example of a data structure of thesecond feature information storage unit 110. A candidate ID foridentifying a candidate of a retrieval target person and the secondfeature information of the candidate are stored in association with eachother in the second feature information storage unit 110. The secondfeature information storage unit 110 stores information regarding atleast one candidate of a retrieval target person. In a case where thereare a plurality of retrieval target persons, as illustrated in FIG. 6C,in the second feature information storage unit 110, a retrieval targetID which is a basis of the candidate may be associated with a candidateID and the second feature information of the candidate.

FIG. 7 is a flowchart illustrating an example of an operation of theinformation processing apparatus 100. FIG. 8 is a flowchart illustratingdetails of a retrieval process in step S110 in FIG. 7 .

First, the collation unit 102 acquires the first feature information 14a (person region feature information) of a retrieval target person (stepS101). The collation unit 102 further acquires the first image 10 (stepS103). The flow proceeds to the retrieval process in step S110 in FIG. 8.

As illustrated in FIG. 8 , in the retrieval process in step S110, first,the collation unit 102 performs a collation process between the firstfeature information 14 a of the retrieval target person and the firstfeature information 14 b of a person region extracted from a personincluded in the first image 10 (step S111). In a case where a collationresult between the first feature information 14 a and the first featureinformation 14 b indicates a match, that is, a region is found of whichthe matching degree with the first feature information 14 a is equal toor more than a reference (YES in step S113), the extraction unit 104generates the second feature information (face information) from theperson included in the first image 10 (step S115). For example, theextraction unit 104 may specify a person region including the region,specify a facial region in the specified person region, and generate thesecond feature information (face information) by extracting featureinformation of the specified facial region. The registration unit 106stores the generated face information in the second feature informationstorage unit 110 (step S117).

In the present example embodiment, in a case where the collation unit102 finds a person matching person region feature information (firstfeature information 14 a) of a retrieval target person from the firstimage 10, the extraction unit 104 generates feature information of afacial region of the person, and the registration unit 106 stores thefeature information in the second feature information storage unit 110.The feature information of the facial region stored in the secondfeature information storage unit 110 is used for a person identificationprocess in image processing. As mentioned above, according to thepresent example embodiment, a retrieval target person can be found fromthe first image 10 captured by the camera 5 by using person regionfeature information (first feature information 14 a) which is obtainedfrom an image in which the face of the retrieval target person is notcaptured, and face feature information of the found person can beextracted and stored.

Second Example Embodiment

FIG. 9 is a functional block diagram illustrating a logicalconfiguration of an information processing apparatus 100 according to asecond example embodiment. The information processing apparatus 100 ofthe present example embodiment is the same as that of theabove-described example embodiment except that information on acandidate of a retrieval target person is acquired by collating secondfeature information stored in the second feature information storageunit 110 with preregistered face recognition information.

The information processing apparatus 100 in FIG. 9 further includes arecognition unit 120 and an acquisition unit 122 in addition to theconfiguration illustrated in FIG. 3 . The recognition unit 120 performsa collation process on the second feature information stored in thesecond feature information storage unit 110 by using a recognitioninformation database 114. The second feature information of a specificperson is registered in association with information regarding theperson in the recognition information database 114. The acquisition unit122 acquires information regarding a person associated with the secondfeature information judged to have the matching degree of a reference ormore, from the recognition information database 114.

FIG. 10 is a diagram illustrating an example of a data structure of therecognition information database 114. A recognition ID for identifying aspecific person (hereinafter, referred to as a registered person) ofwhich recognition information is registered, face feature information(second feature information) indicating a feature of a face of theregistered person, face image data of the registered person, the name ofthe registered person, attribute information of the registered personare registered in association with each other in the recognitioninformation database 114.

The entity of the face image data may be registered in the recognitioninformation database 114, and a path indicating a location where theface image data is stored and a file name may be registered. As will bedescribed later, the face image data is used for screen display, and anoperator may view a face picture displayed on a screen along a name andattribute information.

In the example illustrated in FIG. 9 , the recognition informationdatabase 114 is a part of the information processing apparatus 100. Therecognition information database 114 may be integrated with a main bodyof the information processing apparatus 100, and may be separatedtherefrom. The recognition information database 114 may be included inthe same apparatus as that of second feature information storage unit110, and may be included in different apparatuses. The recognitioninformation database 114 has a database structure, but may have otherdata structures.

The collation unit 102 may have the function of the recognition unit 120of the present example embodiment.

The information processing apparatus 100 may include an outputprocessing unit (not illustrated) which reads identification information(recognition ID or a name) corresponding to face feature informationstored in the recognition information database 114, and performs aprocess of outputting the identification information from an outputunit, in a case where a recognition result in the recognition unit 120indicates a match. An output process will be described later in detail.

FIG. 11 is a flowchart illustrating an example of procedures of arecognition process in the information processing apparatus 100 of thepresent example embodiment.

First, the recognition unit 120 performs a collation process on thesecond feature information stored in the second feature informationstorage unit 110 by using the recognition information database 114 (stepS121). In a case where there is a person judged to have the matchingdegree of a reference or more through the collation process (YES in stepS123), the recognition unit 120 acquires information (a name or thelike) regarding the person from the recognition information database 114(step S125).

In the present example embodiment, the registration unit 106 of theinformation processing apparatus 100 stores a region corresponding to atleast the first feature information 14 a in the first image 10, in thesecond feature information storage unit 110 in association with thesecond feature information.

For example, as illustrated in FIG. 12 , after step S117 in theflowchart of FIG. 8 , the registration unit 106 may store image data ofa person region in the first image 10, in the second feature informationstorage unit 110 in association with the second feature information(face information) (step S119).

FIG. 13 is a diagram illustrating an example of a data structure of thesecond feature information storage unit 110 of the present exampleembodiment. In the present example embodiment, in addition to theinformation described in FIG. 6B, information regarding the matchingdegree calculated in the collation process in step S121 in FIG. 11 ,image data of the person region, and an image ID (in FIG. 13 ,illustrated as an original image ID) for identifying the first image 10from which the person region is acquired may be stored in associationwith each other in the second feature information storage unit 110.

Due to association with the original image ID, the informationprocessing apparatus 100 may acquire a video ID from the first image 10exemplified in FIG. 4B, and may further acquire information such as animaging location from the second image 22 exemplified in FIG. 4A, inassociation with the second feature information. The informationprocessing apparatus 100 may store imaging conditions such as an imaginglocation, an imaging direction, and an imaging time of the camera 5 inassociation with the second feature information in the second featureinformation storage unit 110.

As described above, in the present example embodiment, the recognitionunit 120 performs a collation process with face information registeredin the recognition information database 114 on face information storedin the second feature information storage unit 110, and thus theacquisition unit 122 can acquire information regarding a similar person.As mentioned above, according to the present example embodiment, it ispossible to achieve the same effect as that in the above-describedexample embodiment, and also to acquire information regarding a similarperson from the first image 10 captured by the camera 5 by performing aface recognition process on a candidate resembling a retrieval targetperson by using person region feature information.

Third Example Embodiment

FIG. 14 is a functional block diagram illustrating a logicalconfiguration of an information processing apparatus 100 according to athird example embodiment. The information processing apparatus 100 ofthe present example embodiment is the same as that of the second exampleembodiment except for a configuration in which information regarding adetected candidate is displayed. The information processing apparatus100 further includes a display processing unit 130 in addition to theconfiguration in FIG. 9 . The display processing unit 130 displaysvarious pieces of information on a display apparatus 132 such as aliquid crystal display or an organic electroluminescence (EL) displayconnected to the information processing apparatus 100.

In the present example embodiment, the acquisition unit 122 acquires aplurality of candidate person region images and pieces of information(names or attributes) from the recognition information database 114. Thedisplay processing unit 130 displays each candidate person region imagein an image display section 214, and displays information regarding atleast one person who is detected as a candidate as a result of a facerecognition process in a candidate display section 222 for eachcandidate. Specifically, the display processing unit 130 displays atleast two of a retrieval target display section 202, a retrieval resultdisplay section 210, and a recognition result display section 220 on anidentical screen.

FIG. 15 is a diagram illustrating an example of a result list screen 200displayed by the information processing apparatus 100 of the presentexample embodiment. In the example illustrated in FIG. 15 , theretrieval target display section 202, the retrieval result displaysection 210, and the recognition result display section 220 aredisplayed on the identical result list screen 200. It should be notedthat an example of displaying at least two of the retrieval targetdisplay section 202, the retrieval result display section 210, and therecognition result display section 220 on an identical screen will bedescribed later.

In the present example embodiment, the acquisition unit 122 acquires atleast some information displayed by the display processing unit 130.Such some information includes, for example, the second image 22 fromwhich the first feature information 14 a (person region featureinformation) indicating a feature regarding a region other than the faceof a retrieval target person, the first image 10 (hereinafter, referredto as a candidate person region image in some cases) in which acollation result with the first feature information 14 a (person regionfeature information) indicates a match, and a facial region is detected,and result information. The result information indicates a result ofcollating face information stored in the recognition informationdatabase 114 with second feature information extracted from a facialregion, and is generated by the recognition unit 120. Specifically, theresult information is data stored in a recognition result storage unit116.

The collation result with the first feature information 14 a (personregion feature information) indicating a match is to include, forexample, a region in which the matching degree with the first featureinformation 14 a (person region feature information) is equal to or morethan a reference.

The second image 22 in which a retrieval target person is captured isdisplayed in the retrieval target display section 202. The second image22 displayed in the retrieval target display section 202 is a part ofthe second image 22 including at least a person region of the retrievaltarget person, and may be, for example, an image generated by cuttingthe second image into a rectangular shape including the person region ofthe retrieval target person. The retrieval result display section 210includes at least one candidate display section 212. Each candidatedisplay section 212 includes the image display section 214. A candidateperson region image of the candidate is displayed in the image displaysection 214 along with an imaging condition (for example, an imaginglocation) of the image, and the matching degree with the second image 22displayed in the retrieval target display section 202. The candidateperson region image is at least a part of the first image 10, and maybe, for example, an image generated by cutting the first image into arectangular shape including a person region matching in collation. Aresult of face recognition is displayed in the recognition resultdisplay section 220, and the candidate display section 222 is alsodisplayed for each candidate displayed in the candidate display section212. The candidate display section 222 includes an image display section224. A face image of a person of which the matching degree with thecandidate person region image is high is displayed in the image displaysection 224. Information displayed in the image display section 224 isincluded in the above-described result information.

In the example illustrated in FIG. 15 , the pieces of informationregarding a plurality of candidates are displayed in a descending orderof the matching degree with the first feature information 14 a. Here,the pieces of information regarding a plurality of candidates aredisplayed in a descending order of the matching degree with the secondfeature information in the candidate display section 222. The number ofcandidates displayed in the retrieval result display section 210 and therecognition result display section 220 may be settable on a setting menu(not illustrated) or the like. All candidates of which the matchingdegree is equal to or more than a predetermined reference value may bedisplayed on a list screen or through scrolling. In this case, thepredetermined reference value may be settable on a setting menu.

As illustrated in FIG. 14 , the information processing apparatus 100further includes the recognition result storage unit 116. Therecognition result storage unit 116 may be integrated with a main bodyof the information processing apparatus 100, and may be separatedtherefrom. The recognition result storage unit 116 may be included inthe same apparatus as that of second feature information storage unit110 and the recognition information database 114, and may be included indifferent apparatuses. The recognition result storage unit 116 may havea database structure.

FIG. 16 is a diagram illustrating an example of a data structure of therecognition result storage unit 116 of the present example embodiment. Acandidate ID, a recognition result ID for identifying resultinformation, the matching degree of face recognition, and recognitioninformation ID indicating source recognition information from which theresult information is obtained are registered in the recognition resultstorage unit 116 in association with each other for each candidate.

FIG. 17 is a flowchart illustrating an example of an operation of theinformation processing apparatus 100 of the present example embodiment.First, the recognition process (step S120) described with reference toFIG. 11 is performed, and then the acquisition unit 122 stores theinformation regarding the person acquired in the recognition process inthe recognition result storage unit 116 as result information (stepS127). The display processing unit 130 displays the acquired information(the result list screen 200 in FIG. 15 ) on the display apparatus 132(step S130).

FIG. 18 is a flowchart illustrating an example of detailed procedures ofthe display process in step S130 in FIG. 17 . First, the acquisitionunit 122 acquires an image (second image 22) of the retrieval targetperson from the first feature information storage unit 112 (step S131).The acquisition unit 122 acquires a candidate image (first image 10)including a region of which the matching degree is equal to or more thana reference and a facial region from the second feature informationstorage unit 110 (step S133). The acquisition unit 122 acquires resultinformation from the recognition result storage unit 116 (step S135).

The display processing unit 130 displays the second image 22 acquired instep S131 in the retrieval target display section 202 of the result listscreen 200, displays the candidate image acquired in step S133 in theimage display section 214 of the retrieval result display section 210,and displays the result information acquired in step S135 in therecognition result display section 220 (step S137).

FIG. 19 is a diagram illustrating details of a display example in thecandidate display section 212 in FIG. 15 . The candidate display section212 includes the image display section 214, a candidate ranking displaysection 215, a matching degree display section 216, and an imaginglocation display section 218. An image including the person region 16 ofa candidate is displayed in the image display section 214, and a framesurrounding the person region 16 and a frame surrounding the facialregion 20 may be separately displayed.

FIG. 20 is a diagram illustrating details of a display example in thecandidate display section 222 in FIG. 15 . The candidate display section222 includes the image display section 224, a candidate ranking displaysection 225, a matching degree display section 226, a name displaysection 228, a candidate attribute information display section 229, anda detail display button 230. A face image of a person detected through aface recognition process is displayed in the image display section 224.The name of the person is displayed in the name display section 228. Atleast one of the face image and identification information of the persondetected through the face recognition process is displayed in thecandidate display section 222. It should be noted that theidentification information of the person may be identificationinformation (the recognition ID in FIG. 10 ) in the recognitioninformation database 114, and may be at least one of a name and anattribute.

Attribute information of the person detected through the facerecognition process, for example, the sex and the age thereof aredisplayed in the candidate attribute information display section 229. Adetail display button 230 is an operation button used to display newattribute information of the person. In a case where pressing of thedetail display button 230 is received, a detailed information window 240in FIG. 21 is displayed. Detailed information displayed in the detailedinformation window 240 may be registered in the recognition informationdatabase 114, and may be stored in another storage apparatus inassociation with a recognition ID. As part of result information,information for reaching the detailed information is stored in therecognition result storage unit 116.

As described above, in the present example embodiment, the displayprocessing unit 130 displays, on the identical result list screen 200,an image (second image 22) of a retrieval target person, an image of aperson region of a candidate detected through a collation process withthe first feature information 14 a (person region feature information)in the first image 10 captured by the camera 5, and an image andinformation of a candidate detected through a collation process on afacial region included in the person region using the recognitioninformation database 114.

As mentioned above, according to the present example embodiment, animage of a candidate can be detected in the first image 10 captured bythe camera 5 and be displayed on the basis of an image (second image 22)of a retrieval target person of which a face is not captured, and, withrespect to a facial region in the image of the candidate, a face imageand attribute information of a person registered in the recognitioninformation database 114 can be viewed on the identical result listscreen 200.

Fourth Example Embodiment

In the above-described example embodiment, the configuration has beendescribed in which the retrieval target display section 202, theretrieval result display section 210, and the recognition result displaysection 220 are displayed on the result list screen 200 together. Theinformation processing apparatus 100 of the present example embodimentdisplays the retrieval target display section 202, displays thecandidate display section 212 in which an image of at least onecandidate is displayed or the candidates may be displayed in a switchingmanner in a ranking order and further displays the display section 222in which a face recognition result for a selected candidate isindicated.

FIGS. 22 to 24 are diagrams illustrating examples of procedures of adisplay process in the information processing apparatus 100 of thepresent example embodiment. FIGS. 25A to 25F are diagrams for explainingchanges of a display screen. Hereinafter, a description will be made ofa display process in the information processing apparatus 100 withreference to the drawings.

First, the display processing unit 130 receives specification for aretrieval target person in the second image 22, and displays theretrieval target person in the retrieval target display section 202 of ascreen 300 in FIG. 25A (step S201). For example, in a case where aplurality of persons are included in the second image 22, a frame forspecifying the person region 16 of the target person may be operated onthe second image 22 by an operator to display the target person. Thescreen 300 further includes a retrieval button 302 and a cancel button304.

In the example illustrated in FIG. 25A, there is a single retrievaltarget person, but, in a case where a collation process is performed byusing the first feature information 14 a of a plurality of retrievaltarget persons captured in a plurality of second images 22, a pluralityof images of the plurality of retrieval target persons may be displayedin the retrieval target display section 202. At least one candidatedisplay section 212 may be displayed for each retrieval target person,and a plurality of pieces of information regarding a plurality ofcandidates obtained as a result of a collation process using the firstfeature information 14 a of a plurality of retrieval target persons maybe displayed in the candidate display section 212.

In a case where pressing of the retrieval button 302 is received (YES instep S203), the information processing apparatus 100 performs theretrieval process described in FIG. 8 (step S110). A message 306indicating that retrieval is in progress is displayed during theretrieval process (step S205). In a case where pressing of the cancelbutton 304 is received during the retrieval process, the informationprocessing apparatus 100 stops the retrieval process, and returns tostep S201 (not illustrated).

In a case where a candidate is not found as a result of the retrievalprocess (NO in step S207), a screen 310 in FIG. 25E is displayed. Thescreen 310 includes a message 312 indicating that a candidate is notfound. The screen 310 may include a re-retrieval button 314. In a casewhere pressing of the re-retrieval button 314 is received, the flow mayreturn to step S201, and a message prompting re-selection of the secondimage 22 of a retrieval target person may be displayed such thatre-selection is received (not illustrated).

In the present example embodiment, it is assumed that a candidatedisplay method is set in advance on a setting menu (not illustrated) orthe like. On the setting menu, a method in which detected candidates aredisplayed one by one or a method in which detected candidate are alldisplayed (by a predetermined number of persons (for example, by thethree highest-ranking persons)) may be selected. In a case where atleast one candidate is found (YES in step S207), the informationprocessing apparatus 100 changes a candidate display method according tothe setting (step S211). For example, in a case where all candidates(three highest-ranking persons in this example) are selected, the flowproceeds to the recognition process in step S120, and a face recognitionprocess is performed on a facial region of each candidate. The resultlist screen 200 (FIG. 15 ) indicating a result of the face recognitionprocess is displayed (step S213).

In a case where the setting of sequentially displaying candidates one byone is selected (sequential in step S211), the information processingapparatus 100 proceeds to step S221 (FIG. 23 ). First, 1 is set in acounter i (step S221). In a case where i is equal to or less than 1, asillustrated in FIG. 25B, a next button 332 for displaying the nextcandidate is displayed on a screen 330, and a previous button fordisplaying the previous candidate is not displayed (step S225). Theretrieval target display section 202 and the candidate display section212 of a first candidate are displayed on the screen 330 (step S233). Aretrieval button 334 for receiving a face recognition process for thecandidate is displayed on the screen 330.

A process is changed depending on the type of pressed operation buttonon the screen 330 (step S235). In a case where pressing of the nextbutton 332 is received, i is incremented to 2 (step S243), and the flowreturns to step S223. Since i is 2, a result in step S223 indicates NO,and a previous button 342 is further displayed on a screen 340 (stepS227). Here, assuming that a maximum value of the number of displayedcandidates is 3, i>the maximum value imax −1 (that is, 2) is notestablished (NO in step S229), the flow proceeds to step S233, and thecandidate display section 212 of a second candidate is displayed on ascreen 340 in FIG. 25C (step S233).

In a case where the previous button 342 is pressed (previous in stepS235), i is decremented to 1 (step S241), and the flow returns to stepS223. Since i≤1 is established, the flow proceeds to step S225, the nextbutton 332 is displayed, and the previous button 342 is not displayed.The candidate display section 212 of the first candidate is displayed onthe screen 330 (FIG. 25B) (step S235).

In a case where the retrieval button 334 is pressed on the screen 330 inFIG. 25B (retrieval in step S235), a face recognition process isperformed on a facial region of the first candidate (step S120). In acase where result information is acquired, a result screen 350 in FIG.25D is displayed (step S237). The retrieval target display section 202,the candidate display section 212, and the candidate display section 222are displayed on the result screen 350, and a change button 352 forperforming a face recognition process on other candidates may be furtherdisplayed.

In a case where the change button 352 is pressed (YES in step S239), theflow returns to step S223. Here, since i is 1, the screen 330 in FIG.25B is displayed. In a case where the next button 332 is selected, thesecond candidate is displayed (step S233), and, in a case where theretrieval button 334 is selected (retrieval in step S235), a facerecognition process is performed on the second candidate (step S120).

As another example, candidates may be displayed by a predeterminednumber of persons (for example, by the three highest-ranking persons)instead of one by one, and a person on which a face recognition processis to be performed may be selected from among the candidates. FIG. 24 isa flowchart illustrating an example of procedures of a display process.In a case where a candidate is detected in step S207 in FIG. 22 (YES instep S207), a screen 360 in FIG. 25F is displayed (step S251). Therecognition result display section 220, and the retrieval result displaysection 210 including the candidate display sections 212 of a pluralityof candidates (three persons in this example) are displayed on thescreen 360. A retrieval button 362 for receiving selection of acandidate on which a face recognition process is to be performed isdisplayed in the candidate display section 212.

In a case where the retrieval button 362 is pressed (YES in step S253),the recognition unit 120 performs a face recognition process on theselected candidate (step S120). A result of the face recognition processis displayed on the screen 350 in FIG. 25D. On the screen 350, an imageof the candidate selected as a target of the face recognition process isdisplayed in the candidate display section 212.

As described above, in the present example embodiment, the displayprocessing unit 130 can display the screen 320 or the screen 360including the retrieval target display section 202 and the retrievalresult display section 210. Since, on the screen 320 or the screen 360,an operator can select a candidate on which a face recognition processis to be performed, and can perform the face recognition process, theface recognition process can be omitted on a clearly dissimilar person,and thus efficiency is improved. A display content can be changeddepending on a size or the like of a display, and thus easily viewablescreen display is possible.

FIG. 26 is a diagram illustrating an example of a configuration of acomputer 80 implementing the information processing apparatus 100 ofeach of the above-described example embodiments.

The computer 80 includes a central processing unit (CPU) 82, a memory84, a program 90, loaded to the memory 84, for implementing theconstituent elements of each information processing apparatus 100 inFIGS. 2, 9, and 11 , a storage 85 storing the program 90, aninput/output (I/O) 86, and a network connection interface (communicationI/F 87).

The CPU 82, the memory 84, the storage 85, the I/O 86, and thecommunication I/F 87 are connected to each other through a bus 89, andthe entire information processing apparatus is controlled by the CPU 82.However, a method of connecting the CPU 82 and the like to each other isnot limited to bus connection.

The memory 84 is a memory such as a random access memory (RAM) or a readonly memory (ROM). The storage 85 is a storage apparatus such as a harddisk, a solid state drive (SSD), or a memory card.

The storage 85 may be a memory such as a RAM or a ROM. The storage 85may be provided in the computer 80, may be provided outside the computer80 as long as the computer 80 can assess the storage, and may beconnected to the computer 80 in a wired or wireless manner.Alternatively, the storage may be provided to be attachable to anddetachable from the computer 80.

The CPU 82 reads the program 90 stored in the storage 85 to the memory84 and executes the program, and can thus realize the function of eachunit of the information processing apparatus 100 of each exampleembodiment.

The I/O 86 controls input and output of data and a control signal amongthe computer 80 and other input and output apparatuses. The other inputand output apparatuses include, for example, input apparatuses (notillustrated) such as a keyboard, a touch panel, a mouse, and amicrophone connected to the computer 80, output apparatuses (notillustrated) such as a display, a printer, and a speaker, and aninterface among the computer 80 and the input and output apparatuses.The I/O 86 may control input and output of data with other reading orwriting apparatuses (not illustrated) for a storage medium.

The communication I/F 87 is a network connection interface performingcommunication between the computer 80 and an external apparatus. Thecommunication I/F 87 may be a network interface for connection to acable line, and may be a network interface for connection to a radioline. For example, the computer 80 implementing the informationprocessing apparatus 100 is connected to at least one camera 5 through anetwork by using the communication I/F 87.

Each constituent element of the information processing apparatus 100 ofeach example embodiment is realized by any combination of hardware andsoftware of the computer 80 in FIG. 26 . It is understood by a personskilled in the art that there are various modification examples in arealization method and a realization apparatus. The functional blockdiagram illustrating the information processing apparatus 100 of each ofthe above-described example embodiments indicates a block in the logicalfunctional unit instead of a configuration in the hardware unit.

The information processing apparatus 100 may be configured with aplurality of computers 80, and may be realized by a virtual server.

As the example of being configured with a plurality of computers 80,this disclosure may be realized as an information processing systemexemplified below, but is not limited thereto.

-   -   (1) The extraction unit 104 and the collation unit 102 are        implemented by different apparatuses (computers 80). For        example, the information processing system may include a        terminal apparatus having the extraction unit 104 and a server        apparatus having the collation unit 102.    -   (2) A storage unit implementing the second feature information        storage unit 110 and the first feature information storage unit        112 may be implemented by a plurality of apparatuses. For        example, a storage device storing second feature information        extracted from a person included in the first image may be        provided separately from a storage apparatus storing        identification information and second feature information of a        specific person.    -   (3) Among extraction processes performed by the extraction unit        104, a process of extracting feature information from the first        image 10 and a process of extracting feature information from        the second image 22 are performed by different apparatuses        (computers 80). For example, the information processing system        may include a plurality of terminal apparatuses having the        extraction unit 104 and analyzing videos, and a server apparatus        integrating information therefrom and performing a collation        process. The plurality of terminal apparatuses may be disposed        to be distributed to respective regions, and the extraction unit        102 may be implemented by a plurality of physical apparatuses.    -   (4) Different apparatuses (computers 80) may be used depending        on the type (face feature information and person region feature        information) of feature information which is a target of video        analysis (the extraction unit 104 and the collation unit 102).

As mentioned above, the example embodiments of this disclosure have beendescribed with reference to the drawings, but these are examples of thisdisclosure, and various configurations other than the description may beemployed.

For example, in the above-described example embodiment, a firstcollation process between feature information extracted from a personregion of a person included in the first image 10 and the first featureinformation 14 a of a retrieval target person, and a second collationprocess between second feature information extracted from a facialregion of a person in the first image 10, indicating a match in thecollation process, and face feature information of a registered person,are performed. In other example embodiments, the collation unit 102 mayperform at least either one of the first collation process and thesecond collation process with respect to a third image which isdifferent from the first image 10 and the second image 22 by using thefirst feature information 14 a and the second feature information.

In this configuration, in a case where a facial region can be detectedfrom the third image, the second collation process with the secondfeature information may be performed instead of the first collationprocess with the first feature information 14 a. In a case where afacial region cannot be detected from the third image, the firstcollation process with the first feature information 14 a may beperformed. New second feature information generated through the firstcollation process may be stored in the second feature informationstorage unit 110.

According to this configuration, it is possible to retrieve a retrievaltarget person from the third image by performing a collation processusing the first feature information 14 a or the second featureinformation which is already acquired, on the third image captured atthe date and time or a location which is different from that of thefirst image 10.

In the above-described example embodiment, a description has been madeof an example in which the first feature information is featureinformation extracted from a first portion of a person region, and thesecond feature information is feature information from a second portionof the person region, for example, a facial region, but there may beother combinations of the first feature information and the secondfeature information.

As a first example, the first feature information may be informationindicating at least one of colors of clothes, the age, the sex, and theheight of a person, and the second feature information may be facefeature information of a person.

In this example, for example, in a case where a face of a retrievaltarget person is not captured in the second image 22, and thus facerecognition cannot be performed, instead of performing a process ofextracting the first feature information 14 a from a person region inthe second image 22, at least one of colors of clothes, the age, thesex, and the height of a retrieval target person may be specifiedthrough an operator's operation, and may be stored in the first featureinformation storage unit 112 as the first feature information 14 a. Inthis example, an operation reception portion (not illustrated) forreceiving input of the first feature information of a retrieval targetperson may be provided on a screen along with the retrieval targetdisplay section 202 in which the second image 22 is displayed. Theoperator may input the first feature information of the person whileviewing an image of the retrieval target person displayed in theretrieval target display section 202.

As a second example, the first feature information may be featureinformation of the entire person region, and the second featureinformation may be a plurality of pieces of biological recognitioninformation for performing a multimodal biological recognition processin which a plurality of pieces of biological information are combinedwith each other.

This example may be applied, for example, in a case where a face of aretrieval target person is not captured in the second image 22, and thusface recognition is hard to perform, and the lower half of the body isnot also captured, and thus gait recognition is also hard to perform.The first feature information 14 a may be extracted from a person regionof a person captured in the second image 22, a candidate may be detectedfrom the first image 10 by using the first feature information 14 a, anda person may be identified through a multimodal biological recognitionprocess on the detected candidate by using a plurality of pieces ofbiological information as the second feature information.

As mentioned above, this disclosure has been described with reference tothe example embodiments and the Examples, but this disclosure is notlimited to the example embodiments and Examples. The configuration ordetails of this disclosure may be subjected to various changes which canbe understood by a person skilled in the art within the scope of thisdisclosure.

It should be noted that acquisition and use of information regarding auser in this disclosure are assumed to be performed legally.

Some or all of the above-described example embodiments may be disclosedas in the following appendix, but are not limited thereto.

-   -   1. An information processing apparatus including:    -   a collation unit that collates first feature information        extracted from a person included in a first image with first        feature information indicating a feature of a retrieval target        person;    -   an extraction unit that extracts second feature information from        the person included in the first image in a case where a        collation result in the collation unit indicates a match; and    -   a registration unit that stores, in a storage unit, the second        feature information extracted from the person included in the        first image.    -   2. The information processing apparatus according to 1.,    -   in which the collation unit collates the second feature        information extracted from the person included in the first        image with the second feature information stored in the storage        unit in association with identification information of a        specific person,    -   the information processing apparatus further including an output        processing unit that outputs the identification information        stored in the storage unit from an output unit in a case where a        collation result indicates a match.    -   3. The information processing apparatus according to 1. or 2.,    -   in which the first feature information is generated by using a        second image which is different from the first image.    -   4. The information processing apparatus according to 3.,    -   in which the extraction unit extracts a plurality of pieces of        the first feature information from person regions of persons        included in a plurality of the second images which are different        from each other,    -   in which the registration unit stores the plurality of pieces of        first feature information in the storage unit in association        with each other as information regarding an identical person,        and    -   in which the collation unit performs the collation process by        using the plurality of pieces of first feature information        stored in the storage unit.    -   5. The information processing apparatus according to 3. or 4.,    -   in which the second image is generated by an imaging unit which        is different from an imaging unit generating the first image.    -   6. The information processing apparatus according to any one        of 1. to 5.,    -   in which the collation unit performs at least one of a collation        process with respect to a third image with the first feature        information and a collation process with the second feature        information.    -   7. The information processing apparatus according to any one        of 1. to 6.,    -   in which the registration unit stores a region corresponding to        at least the first feature information in the first image, in        the storage unit in association with the second feature        information.    -   8. The information processing apparatus according to any one        of 1. to 7.,    -   in which the registration unit stores the second feature        information in association with an imaging condition of the        first image.    -   9. The information processing apparatus according to any one        of 1. to 8.,    -   in which the second feature information is feature information        of a face of a person, and the first feature information is        feature information including a region other than the face of        the person.    -   10. An information processing system including:    -   a collation unit that collates first feature information        extracted from a person included in a first image with first        feature information indicating a feature of a retrieval target        person;    -   an extraction unit that extracts second feature information from        the person included in the first image in a case where a        collation result in the collation unit indicates a match; and    -   a registration unit that stores, in a storage unit, the second        feature information extracted from the person included in the        first image.    -   11. The information processing apparatus according to 10.,    -   in which the collation unit collates the second feature        information extracted from the person included in the first        image with the second feature information stored in the storage        unit in association with identification information of a        specific person,    -   the information processing system further including an output        processing unit that outputs the identification information        stored in the storage unit from an output unit in a case where a        collation result indicates a match.    -   12. The information processing system according to 10. or 11.,    -   in which the first feature information is generated by using a        second image which is different from the first image.    -   13. The information processing system according to 12.,    -   in which the extraction unit extracts a plurality of pieces of        the first feature information from person regions of persons        included in a plurality of the second images which are different        from each other,    -   in which the registration unit stores the plurality of pieces of        first feature information in the storage unit in association        with each other as information regarding an identical person,        and    -   in which the collation unit performs the collation process by        using the plurality of pieces of first feature information        stored in the storage unit.    -   14. The information processing system according to 12. or 13.,    -   in which the second image is generated by an imaging unit which        is different from an imaging unit generating the first image.    -   15. The information processing system according to any one        of 10. to 14.,    -   in which the collation unit performs at least one of a collation        process with respect to a third image with the first feature        information and a collation process with the second feature        information.    -   16. The information processing system according to any one        of 10. to 15.,    -   in which the registration unit stores a region corresponding to        at least the first feature information in the first image, in        the storage unit in association with the second feature        information.    -   17. The information processing system according to any one        of 10. to 16.,    -   in which the registration unit stores the second feature        information in association with an imaging condition of the        first image.    -   18. The information processing system according to any one        of 10. to 17.,    -   in which the second feature information is feature information        of a face of a person, and the first feature information is        feature information including a region other than the face of        the person.    -   19. An information processing method executer by an information        processing apparatus, the method including:    -   collating first feature information extracted from a person        included in a first image with first feature information        indicating a feature of a retrieval target person;    -   extracting second feature information from the person included        in the first image in a case where a collation result indicates        a match, and    -   storing, in a storage unit, the second feature information        extracted from the person included in the first image.    -   20. The information processing method executer by the        information processing apparatus according to 19., the method        including:    -   collating the second feature information extracted from the        person included in the first image with the second feature        information stored in the storage unit in association with        identification information of a specific person; and    -   outputting the identification information stored in the storage        unit from an output unit in a case where a collation result        indicates a match.    -   21. The information processing method according to 19. or 20.,    -   in which the first feature information is generated by using a        second image which is different from the first image.    -   22. The information processing method executer by the        information processing apparatus according to 21., the method        including:    -   extracting a plurality of pieces of the first feature        information from person regions of persons included in a        plurality of the second images which are different from each        other;    -   storing the plurality of pieces of first feature information in        the storage unit in association with each other as information        regarding an identical person; and    -   performing the collation process by using the plurality of        pieces of first feature information stored in the storage unit.    -   23. The information processing method according to 21. or 22.,    -   in which the second image is generated by an imaging unit which        is different from an imaging unit generating the first image.    -   24. The information processing method executer by the        information processing apparatus according to any one of 19. to        23., the method including:    -   performing at least one of a collation process with respect to a        third image with the first feature information and a collation        process with the second feature information.    -   25. The information processing method executer by the        information processing apparatus according to any one of 19. to        24., the method including:    -   storing a region corresponding to at least the first feature        information in the first image, in the storage unit in        association with the second feature information.    -   26. The information processing method executer by the        information processing apparatus according to any one of 19. to        25., the method including:    -   storing the second feature information in association with an        imaging condition of the first image.    -   27. The information processing method according to any one        of 19. to 26.,    -   in which the second feature information is feature information        of a face of a person, and the first feature information is        feature information including a region other than the face of        the person.    -   28. A program causing a computer to execute:    -   a procedure of collating first feature information extracted        from a person included in a first image with first feature        information indicating a feature of a retrieval target person;    -   a procedure of extracting second feature information from the        person included in the first image in a case where a collation        result indicates a match; and    -   a procedure of storing, in a storage unit, the second feature        information extracted from the person included in the first        image.    -   29. The program according to 28., causing a computer to execute:    -   a procedure of collating the second feature information        extracted from the person included in the first image with the        second feature information stored in the storage unit in        association with identification information of a specific        person; and    -   a procedure of outputting the identification information stored        in the storage unit from an output unit in a case where a        collation result indicates a match.    -   30. The program according to 28. or 29.,    -   in which the first feature information is generated by using a        second image which is different from the first image.    -   31. The program according to 30., causing the computer to        execute:    -   a procedure of extracting a plurality of pieces of the first        feature information from person regions of persons included in a        plurality of the second images which are different from each        other;    -   a procedure of storing the plurality of pieces of first feature        information in the storage unit in association with each other        as information regarding an identical person; and    -   a procedure of performing the collation process by using the        plurality of pieces of first feature information stored in the        storage unit.    -   32. The program according to 30. or 31.,    -   in which the second image is generated by an imaging unit which        is different from an imaging unit generating the first image.    -   33. The program according to any one of 28. to 32., causing the        computer to execute    -   a procedure of performing at least one of a collation process        with respect to a third image with the first feature information        and a collation process with the second feature information.    -   34. The program according to any one of 28. to 33., causing the        computer to execute    -   a procedure of storing a region corresponding to at least the        first feature information in the first image, in the storage        unit in association with the second feature information.    -   35. The program according to any one of 28. to 34., causing the        computer to execute    -   a procedure of storing the second feature information in        association with an imaging condition of the first image.    -   36. The program according to any one of 28. to 35.,    -   in which the second feature information is feature information        of a face of a person, and the first feature information is        feature information including a region other than the face of        the person.

1. An information processing apparatus comprising: at least one memoryconfigured to store instructions; and at least one processor configuredto execute the instructions to: perform a first collation process ofcollating first feature information extracted from a person included ina first image with first feature information indicating feature of aregion including a region other than a face of a retrieval target personto acquire a first collation result; extract second feature informationindicating feature of a face from the person as a candidate included inthe first image in a case where the first collation result indicates amatch; store the extracted second feature information of the candidatein association with the corresponding retrieval target person in a firststorage unit; perform a second collation process of collating the secondfeature information of the candidate with the second feature informationstored in a second storage unit in association with identificationinformation of a specific person to acquire a second collation result;and in a case where the feature information of the face is not extractedas the second feature information of the candidate, perform the secondcollation process using biological recognition information of thecandidate with the second feature information stored in a second storageunit in association with identification information of a specific personto acquire the second collation result; and cause an output unit tooutput the identification information stored in the second storage unitfrom an output unit in a case where the second collation resultindicates a match.
 2. The information processing apparatus according toclaim 1, wherein the processor is further configured to execute theinstructions to perform the second collation process by performing amultimodal biological recognition process using a plurality of pieces ofbiological recognition information.
 3. The information processingapparatus according to claim 1, wherein the first feature information isgenerated by using a second image which is different from the firstimage.
 4. The information processing apparatus according to claim 3,wherein the processor is further configured to execute the instructionsto: extract a plurality of pieces of the first feature information fromperson regions of persons included in a plurality of second images whichare different from each other; store the plurality of pieces of firstfeature information in the first storage unit in association with eachother as information regarding an identical person; and perform thefirst collation process by using the plurality of pieces of firstfeature information stored in the first storage unit.
 5. The informationprocessing apparatus according to claim 4, wherein the second image isgenerated by an imaging unit which is different from an imaging unitgenerating the first image.
 6. The information processing apparatusaccording to claim 1, wherein the processor is further configured toexecute the instructions to perform at least one of the first collationprocess and the second collation process with respect to a third image.7. The information processing apparatus according to claim 1, whereinthe processor is further configured to execute the instructions to storea region corresponding to at least the first feature information in thefirst image, in the first storage unit in association with the secondfeature information.
 8. The information processing apparatus accordingto claim 1, wherein the processor is further configured to execute theinstructions to store the second feature information in association withan imaging condition of the first image.
 9. An information processingmethod comprising: performing a first collation process of collatingfirst feature information extracted from a person included in a firstimage with first feature information indicating feature of a regionincluding a region other than a face of a retrieval target person toacquire a first collation result; extracting second feature informationindicating feature of a face from the person as a candidate included inthe first image in a case where the first collation result indicates amatch; storing the extracted second feature information of the candidatein association with the corresponding retrieval target person in a firststorage unit; performing a second collation process of collating thesecond feature information of the candidate with the second featureinformation stored in a second storage unit in association withidentification information of a specific person to acquire a secondcollation result; and in a case where the feature information of theface is not extracted as the second feature information of thecandidate, performing the second collation process using biologicalrecognition information of the candidate with the second featureinformation stored in a second storage unit in association withidentification information of a specific person to acquire the secondcollation result; and causing an output unit to output theidentification information stored in the second storage unit from anoutput unit in a case where the second collation result indicates amatch.
 10. A non-transitory computer-readable storage medium storing aprogram causing a computer to execute: a procedure of performingperforming a first collation process of collating first featureinformation extracted from a person included in a first image with firstfeature information indicating feature of a region including a regionother than a face of a retrieval target person to acquire a firstcollation result; a procedure of extracting second feature informationindicating feature of a face from the person as a candidate included inthe first image in a case where the first collation result indicates amatch; a procedure of storing the extracted second feature informationof the candidate in association with the corresponding retrieval targetperson in a first storage unit; a procedure of performing a secondcollation process of collating the second feature information of thecandidate with the second feature information stored in a second storageunit in association with identification information of a specific personto acquire a second collation result; and in a case where the featureinformation of the face is not extracted as the second featureinformation of the candidate, a procedure of performing the secondcollation process using biological recognition information of thecandidate with the second feature information stored in a second storageunit in association with identification information of a specific personto acquire the second collation result; and a procedure of causing anoutput unit to output the identification information stored in thesecond storage unit from an output unit in a case where the secondcollation result indicates a match.